Technical Field
The present invention relates to chemical structure design and more particularly to designing chemical structures that can possess multiple intended chemical and physical properties.
Description of Related Art
The combination of chemical elements to produce new elemental compounds has long been researched through experimentation and study. As new chemical compositions are discovered, the physical and chemical properties of the compositions are analyzed. Many of these physical and chemical properties have been found, in one form or another, to be beneficial.
Until this point in time, there have only been roughly 109 discovered materials. When compared to the more than 1062 materials yet to be discovered, the number of discovered materials is only a very small fraction of the total amount of possible materials. These yet to be discovered materials could possess a vast amount of beneficial physical and chemical properties. However, not every material possesses beneficial physical and chemical properties. Therefore, there is an increase in demand to for the discovery and design of new materials so that the materials with beneficial physical and chemical properties can be determined.
Conventionally, researchers and engineers had performed material design. The researchers and engineers would use their intuition and the repetition of trial-and-error experimentation and chemical simulation in order to produce new materials with new physical and chemical properties. However, these conventional methods were very time-consuming and were not conductive to the exploration and study of the vast parameter space of undiscovered materials.
Recently, machine learning has been applied in an attempt to make the discovery and design of new materials more efficient. However, most of these machine-learning methods base their reports of the predicted physical and chemical properties of materials on the structural information of the materials. This indicates that the structures of the materials must first be determined before such machine learning can take place. There are thus few reports concerning machine learning that result in the prediction of the structure of a material based on intended physical and chemical properties.
Some methods report on chemical structure prediction through regression. However, due to the nature of regression, the input and output of a regression model are constrained to be in the vector and scalar form, respectively. Therefore, in such a report, a system receives a structural feature vector and predicts its chemical feature, creating a constructed model. On the constructed model, structural feature parameters are swept in order to determine the structural parameter set that meets the intended property. By sweeping the parameter set, this grid search requires a very large amount of computation power when the parameter set has a high dimension. Furthermore, it is impossible to construct a model to predict multiple chemical features using such a system because the output is restricted to be a scalar value. Additionally, this method is limited to only inorganic materials.
Some other known methods perform chemical structure prediction of organic materials. However, such methods are limited to the prediction of a structure that possesses a chemical value that ranges only in existing materials. This is because such methods utilize a kernel method, which is based on a similarity search, to analyze chemical structures.
Therefore, there is a need for a system and method for designing chemical structures that can possess multiple intended chemical and physical properties by utilizing machine learning.